This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. There is an urgent need for biomarkers and assessment tools of renal disease. According to the Journal of the American Medical Association (2007), 13% of American adults suffer from chronic kidney disease (CKD), an increase of 30% over the previous decade, and CKD and end-stage renal disease consume 27% of the Medicare budget. Moreover, obesity, even in the absence of diabetes, is a known risk factor for CKD, and 34% of Americans are considered obese (BMI >30). Early detection of obesity-related chronic kidney disease can lead to obesity treatment and stabilization of renal function. Urine is an excellent body fluid for detecting kidney disease [unreadable]it can be easily obtained in a non-invasive and risk-free manner, is relatively stable compared to plasma, and is highly enriched for metabolites and protein fragments that originate in the kidneys and urinary tract. With this application, the applicants seek to collaborate with Drs. Mikhail Belov and Richard Smith at the Proteomics Research Resource in Integra-tive Biology in order to develop advanced mass spectrometric and computational methodologies for the discovery of urinary proteomic and metabolomic biomarkers of obesity-related renal disease and for quantitative assessment of patient response to therapeutic intervention. The strategic goal of this project is to acquire support to conduct a large-scale (>1,000 patients), multi-year set of trials to identify panels of urinary biomarkers that are highly sensitive and specific for obesity-related CKD using mass spectrometry and advanced statistical methods and to validate their predictive value with blinded sample sets and estimates of renal disease prevalence. The applicants have initiated a collaboration with physicians at Samaritan Kidney Specialists (Samaritan Health Services, Corvallis, Oregon) to collect and analyze a training set of urine samples from obese patients using proteomic and metabolomic technologies. However, larger sample sets and advanced methodology will ultimately be required to establish a clinical basis for diagnosis. The applicants hypothesize that some combination of urinary proteomic and metabolomic markers of obesity-related CKD will increase the sensitivity and specificity of detecting and treating renal insufficiency in its early stages in this at risk patient group.